The temporal signature of self: Temporal measures of resting‐state EEG predict self‐consciousness

Private self-consciousness—the tendency to focus on one's inner thoughts and feelings—is linked to specific temporal patterns in the brain's resting state activity. In an EEG study, higher scores on the Private dimension of the Self-Consciousness Scale correlated with three measures of resting state activity: a steeper power-law exponent (PLE), a longer auto-correlation window (ACW), and a stronger modulation index (MI). These temporal features reflect how self-related information is temporally nested, continuous, and integrated in spontaneous brain activity. Source localization further linked Private self-consciousness to activity in cortical midline structures, including the perigenual anterior cingulate cortex and posterior cingulate cortex. A machine learning algorithm could accurately classify individuals as having high or low Private self-consciousness based on these spatiotemporal measures.

Study at a glance

Abstract

The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self-related activity and resting state activity. This suggests that information about self-relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such "rest-self containment," however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self-consciousness. This was obtained with the self-consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self-consciousness and three temporal measures of resting state activity: scale-free activity as indexed by the power-law exponent (PLE), the auto-correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self-consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self-consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data-driven analysis; a machine learning algorithm accurately predicted an individual as having a "high" or "low" Private self-consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self-consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that self-related information is temporally contained in the brain's resting state. "Rest-self containment" can thus be featured by a temporal signature.